Abstract

This study explores the novel methodology AI-Readiness and Market Dynamics Disruption Index (AIM-DDI) Framework. This framework assesses an organization's preparedness for artificial intelligence (AI) adoption and its capacity to navigate AI-driven market disruptions. AI's rapid evolution has made it a crucial driver of business transformation and competitive edge. However, organizations face challenges in understanding their AI integration readiness and potential AI-driven market disruptions. The AIM-DDI Framework evaluates an organization's internal capabilities, such as AI strategy, infrastructure, talent, imple­mentation, and ethics. Additionally, it examine­s external market dynamics like customer expectations, competitors' AI maturity, industry disruption potential, regulatory landscape, and AI tale­nt ecosystem. The AIM-DDI Framework offers valuable insights by providing a comprehensive view of an organization's position and potential strate­gic directions in the AI-driven landscape­. We will present the initial survey results validating the framework and demonstrating its practical application and effectiveness in assessing organizational AI readiness. The AIM-DDI Framework is willing to serve as a holistic toolkit for organizations to identify strengths, areas for improve­ment, and strategies to le­verage AI for sustainable competitive success.

Abstract

The adoption of artificial intelligence (AI) is transforming and disrupting industries by driving innovation, enhancing operational efficiency, and reshaping market dynamics. However, organizations often struggle to balance internal readiness with external market alignment, creating a critical need for comprehensive frameworks to guide AI integration. This study introduces and validates the AI-Readiness and Market Dynamics Disruption Index (AIM-DDI), a holistic tool for evaluating organizational AI maturity. By combining internal factors, such as strategy, talent, and governance, with external elements, including customer expectations, industry disruption potential, and regulatory environments, the AIM-DDI provides actionable insights for navigating AI-driven transformation. The research validates the AIM-DDI scale and questionnaire, leveraging data from 765 organizations to identify distinct AI maturity profiles. Cronbach’s Alpha Analysis , Exploratory and Confirmatory Factor Analyses confirm the framework’s validity, while cluster analysis reveals strategic pathways for organizations at various stages of AI readiness. The findings highlight the importance of aligning internal capabilities with external pressures to achieve competitive advantage and sustainable growth. We also apply the AIM-DDI framework within the Beauty Clusters Industry Association in Spain data as business case. This study offers valuable contributions for academics, practitioners, and policymakers aiming to leverage AI as a driver of industry evolution.